Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D denoising kernel

Purpose: Reconstructed PET images are typically noisy, especially in dynamic imaging where the acquired data are divided into several short temporal frames. High noise in the reconstructed images translates to poor precision/reproducibility of image features. One important role of “denoising” is therefore to improve the precision of image features. However, typical denoising methods achieve noise reduction at the expense of accuracy. In this work, we present a novel four-dimensional (4D) denoised image reconstruction framework, which we validate using 4D simulations, experimental phantom, and clinical patient data, to achieve 4D noise reduction while preserving spatiotemporal patterns/minimizing error introduced by denoising. Methods: Our proposed 4D denoising operator/kernel is based on HighlY constrained backPRojection (HYPR), which is applied either after each update of OSEM reconstruction of dynamic 4D PET data or within the recently proposed kernelized reconstruction framework inspired by kernel methods in machine learning. Our HYPR4D kernel makes use of the spatiotemporal high frequency features extracted from a 4D composite, generated within the reconstruction, to preserve the spatiotemporal patterns and constrain the 4D noise increment of the image estimate. Results: Results from simulations, experimental phantom, and patient data showed that the HYPR4D kernel with our proposed 4D composite outperformed other denoising methods, such as the standard OSEM with spatial filter, OSEM with 4D filter, and HYPR kernel method with the conventional 3D composite in conjunction with recently proposed High Temporal Resolution kernel (HYPRC3D-HTR), in terms of 4D noise reduction while preserving the spatiotemporal patterns or 4D resolution within the 4D image estimate. Consequently, the error in outcome measures obtained from the HYPR4D method was less dependent on the region size, contrast, and uniformity/functional patterns within the target structures compared to the other methods. For outcome measures that depend on spatiotemporal tracer uptake patterns such as the nondisplaceable Binding Potential (BPND), the root mean squared error in regional mean of voxel BPND values was reduced from ~8% (OSEM with spatial or 4D filter) to ~3% using HYPRC3D-HTR and was further reduced to ~2% using our proposed HYPR4D method for relatively small target structures (~10 mm in diameter). At the voxel level, HYPR4D produced two to four times lower mean absolute error in BPND relative to HYPRC3D-HTR. Conclusion: As compared to conventional methods, our proposed HYPR4D method can produce more robust and accurate image features without requiring any prior information

Measuring dopaminergic function in the 6-OHDA-lesioned rat: a comparison of PET and microdialysis

BACKGROUND: [(18) F]fluorodopa (FDOPA) positron emission tomography (PET) allows assessment of levodopa (LDOPA) metabolism and is widely used to study Parkinson's disease. We examined how [(18) F]FDOPA PET-derived kinetic parameters relate the dopamine (DA) and DA metabolite content of extracellular fluid measured by microdialysis to aid in the interpretation of data from both techniques. METHODS: [(18) F]FDOPA PET imaging and microdialysis measurements were performed in unilaterally 6-hydroxydopamine-lesioned rats (n = 8) and normal control rats (n = 3). Microdialysis testing included baseline measurements and measurements following acute administration of LDOPA. PET imaging was also performed using [(11)C]dihydrotetrabenazine (DTBZ), which is a ligand for the vesicular monoamine transporter marker and allowed assessment of denervation severity. RESULTS: The different methods provided highly correlated data. Lesioned rats had reduced DA metabolite concentrations ipsilateral to the lesion (p < 0.05 compared to controls), with the concentration being correlated with FDOPA's effective distribution volume ratio (EDVR; r = 0.86, p < 0.01) and DTBZ's binding potential (BP(ND); r = 0.89, p < 0.01). The DA metabolite concentration in the contralateral striatum of severely (>80%) lesioned rats was lower (p < 0.05) than that of less severely lesioned rats (<80%) and was correlated with the ipsilateral PET measures (r = 0.89, p < 0.01 for BP(ND)) but not with the contralateral PET measures. EDVR and BP(ND) in the contralateral striatum were not different from controls and were not correlated with the denervation severity. CONCLUSIONS: The demonstrated strong correlations between the PET and microdialysis measures can aid in the interpretation of [(18) F]FDOPA-derived kinetic parameters and help compare results from different studies. The contralateral striatum was affected by the lesioning and so cannot always serve as an unaffected control

Modeling of [F-18]FEOBV Pharmacokinetics in Rat Brain

Purpose: [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) is a radioligand for the vesicular acetylcholine transporter (VAChT), a marker of the cholinergic system. We evaluated the quantification of [18F]FEOBV in rats in control conditions and after partial saturation of VAChT using plasma and reference tissue input models and test-retest reliability. Procedure: Ninety-minute dynamic [18F]FEOBV PET scans with arterial blood sampling were performed in control rats and rats pretreated with 10 μg/kg FEOBV. Kinetic analyses were performed using one- (1TCM) and two-tissue compartmental models (2TCM), Logan and Patlak graphical analyses with metabolite-corrected plasma input, reference tissue Patlak with cerebellum as reference tissue, standard uptake value (SUV) and SUV ratio (SUVR) using 60- or 90-min acquisition. To assess test-retest reliability, two dynamic [18F]FEOBV scans were performed 1 week apart. Results: The 1TCM did not fit the data. Time-activity curves were more reliably estimated by the irreversible than the reversible 2TCM for 60 and 90 min as the influx rate Ki showed a lower coefficient of variation (COV, 14–24 %) than the volume of distribution VT (16–108 %). Patlak graphical analysis showed a good fit to the data for both acquisition times with a COV (12–27 %) comparable to the irreversible 2TCM. For 60 min, Logan analysis performed comparably to both irreversible models (COV 14–32 %) but showed lower sensitivity to VAChT saturation. Partial saturation of VAChT did not affect model selection when using plasma input. However, poor correlations were found between irreversible 2TCM and SUV and SUVR in partially saturated VAChT states. Test-retest reliability and intraclass correlation for SUV were good. Conclusion: [18F]FEOBV is best modeled using the irreversible 2TCM or Patlak graphical analysis. SUV should only be used if blood sampling is not possible

Effect of dopamine D2 receptor antagonists on [18F]-FEOBV binding

The interaction of dopaminergic and cholinergic neurotransmission in, e.g., Parkinson's disease has been well established. Here, D2 receptor antagonists were used to assess changes in [18F]-FEOBV binding to the vesicular acetylcholine transporter (VAChT) in rodents using positron emission tomography (PET). After pretreatment with either 10 mg/kg haloperidol, 1 mg/kg raclopride, or vehicle, 90 min dynamic PET scans were performed with arterial blood sampling. The net influx rate (Ki) was obtained from Patlak graphical analysis, using a metabolite-corrected plasma input function and dynamic PET data. [18F]-FEOBV concentration in whole-blood or plasma and the metabolite-corrected plasma input function were not significantly changed by the pretreatments (adjusted p > 0.07, Cohen's d 0.28-1.89) while the area-under-the-curve (AUC) of the parent fraction of [18F]-FEOBV was significantly higher after haloperidol treatment (adjusted p = 0.022, Cohen's d = 2.51) than in controls. Compared to controls, the AUC of [18F]-FEOBV, normalized for injected dose and body weight, was nonsignificantly increased in the striatum after haloperidol (adjusted p = 0.4, Cohen's d = 1.77) and raclopride (adjusted p = 0.052, Cohen's d = 1.49) treatment, respectively. No changes in the AUC of [18F]-FEOBV were found in the cerebellum (Cohen's d 0.63-0.74). Raclopride treatment nonsignificantly increased Ki in the striatum 1.3-fold compared to control rats (adjusted p = 0.1, Cohen's d = 1.1) while it reduced Ki in the cerebellum by 28% (adjusted p = 0.0004, Cohen's d = 2.2) compared to control rats. Pretreatment with haloperidol led to a nonsignificant reduction in Ki in the striatum (10%, adjusted p = 1, Cohen's d = 0.44) and a 40-50% lower Ki than controls in all other brain regions (adjusted p < 0.0005, Cohen's d = 3.3-4.7). The changes in Ki induced by the selective D2 receptor antagonist raclopride can in part be quantified using [18F]-FEOBV PET imaging. Haloperidol, a nonselective D2/σ receptor antagonist, either paradoxically decreased cholinergic activity or blocked off-target [18F]-FEOBV binding to σ receptors. Hence, further studies evaluating the binding of [18F]-FEOBV to σ receptors using selective σ receptor ligands are necessary

NEMA NU 4-2008 Comparison of preclinical PET imaging systems

The National Electrical Manufacturers Association (NEMA) standard NU 4-2008 for performance measurements of smallanimal tomographs was recently published. Before this standard, there were no standard testing procedures for preclinical PET systems, and manufacturers could not provide clear specifications similar to those available for clinical systems under NEMA NU 2-1994 and 2-2001. Consequently, performance evaluation papers used methods that were modified ad hoc from the clinical PET NEMA standard, thus making comparisons between systems difficult. Methods: We acquired NEMA NU 4-2008 performance data for a collection of commercial animal PET systems manufactured since 2000: micro- PET P4, microPET R4, microPET Focus 120, microPET Focus 220, Inveon, ClearPET, Mosaic HP, Argus (formerly eXplore Vista), VrPET, LabPET 8, and LabPET 12. The data included spatial resolution, counting-rate performance, scatter fraction, sensitivity, and image quality and were acquired using settings for routine PET. Results: The data showed a steady improvement in system performance for newer systems as compared with first-generation systems, with notable improvements in spatial resolution and sensitivity. Conclusion: Variation in system design makes direct comparisons between systems from different vendors difficult. When considering the results from NEMA testing, one must also consider the suitability of the PET system for the specific imaging task at hand.This work was funded by the Natural Sciences and Engineering Research Council of Canada under Discovery Grant 341628-2007. No other potential conflict of interest relevant to this article was reported.En prens

System matrix modeling of externally tracked motion

Background and aim In high-resolution emission tomography imaging, even small patient movements can considerably degrade image quality. The aim of this work was to develop a general approach to motion-corrected reconstruction of motion-contaminated data in the case of rigid motion (particularly brain imaging) which would be applicable to any PET scanner in the field, without specialized data-acquisition requirements. Methods Assuming the ability to externally track subject motion during scanning (e.g., using the Polaris camera), we proposed to incorporate the measured rigid motion information into the system matrix of the expectation maximization reconstruction algorithm. Furthermore, we noted and developed a framework to incorporate the additional effect of motion on modifying the attenuation factors. A new mathematical brain phantom was developed and used along with elaborate combined Simset/GATE simulations to compare the proposed framework with the cases of no motion correction. Results and conclusion Clear qualitative and quantitative improvements were observed when incorporating the proposed framework. The method is very practical to implement for any scanner in the field, not requiring any hardware modifications or access to the list-mode acquisition capability

The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.Purpose: The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods: Fifty-seven subjects with significant white matter disease presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a mul- ticenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to gen- erate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for su- pervised training of RFs programmed in MATLAB. Results: 18F-Florbetapir PETs were randomly divided into 40 training and 17 testing scans; 100 RFs of 1000 trees, constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: right posterior cingulate gyrus, right anterior cingulate gyrus, left precuneus, left posterior cingulate gyrus, and right precuneus. Amyloid in- creased with decreasing MoCA score. 18F-FDG PETs were randomly di- vided into 40 training and 15 testing scans; 100 RFs of 1000 trees, each tree constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: left fusiform gyrus, left precuneus, left posterior cingulate gyrus, right precuneus, and left middle orbitofrontal gyrus. 18F-FDG decreased with decreasing MoCA score. Conclusions: Random forests help pinpoint clinically relevant ROIs associ- ated with MoCA score; amyloid increased and 18F-FDG decreased with de- creasing MoCA score, most significantly in the posterior cingulate gyrus.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

The use of random forests to classify amyloid brain PET

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB. Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals